Automatic Adaptation of Parameters Controlling Database Savepoints

ABSTRACT

Each of a plurality of database transactions are logged (i.e., recorded) in a log. Concurrent with the logging, one or more characteristics of the log are monitored. Thereafter, a savepoint is triggered when a pre-defined condition is met as indicated by the monitoring. The triggered savepoint can override or accelerate a savepoint that would have otherwise been triggered based on pre-specified parameters.

TECHNICAL FIELD

The subject matter described herein relates to enhanced techniques fordatabase recovery that provides for automatic adaptation of parameterscontrolling savepoints.

BACKGROUND

Database systems are susceptible to failure for a variety of reasonsincluding both software and hardware related issues. As a result,recovery logs that record various operations performed by such databasesystems have been adopted. These recovery logs record various actionsperformed by the database systems which can be later replayed, ifneeded, as part of a recovery operation. The point at which atransaction can be rolled back to can be referred to as a savepoint. Ifan error occurs in the midst of a multiple-statement transaction, thedatabase system can recover from the error by rolling back to a mostrecent savepoint without needing to abort the entire transaction.

SUMMARY

In one aspect, each of a plurality of database transactions are logged(i.e., recorded) in a log. Concurrent with the logging, one or morecharacteristics of the log are monitored. Thereafter, a savepoint istriggered when a pre-defined condition is met as indicated by themonitoring. The triggered savepoint can override or accelerate asavepoint that would have otherwise been triggered based onpre-specified parameters. Stated differently, triggering of thesavepoint can override savepoint parameters that would otherwise specifywhen a next savepoint is to occur.

The monitoring can monitor an absolute size of the log. In such cases,the pre-defined condition can specify a value for the absolute size ofthe log. The monitoring can additionally or alternatively monitor afilling degree of the log. With such variations, the pre-definedcondition can specify a value for the filling degree of the log. Furtherin addition or in the alternative, the monitoring can monitor an amountof log transaction written since a last savepoint such that thepre-defined condition specifies a value for the amount of logtransactions written since the last savepoint.

In some implementations, the monitoring monitors (i) an amount of loggenerated, (ii) filling of specific log areas, (iii) a number and/orsize of modified pages which need to be written to disk, and (iv) andtimes measured for previous savepoints. Based on such monitoring, thesavepoint is triggered to minimize critical-phase time and an amount oflog.

The database system can include or be an in-memory database storing datain main memory.

The database system can include a primary database system and anassociated secondary database system. With such an arrangement, readstatements can be routed to the secondary database system until suchtime as a result lag between the primary database system is beyond apre-defined lag threshold relative to the secondary database system.Both of the primary database system and the associated secondarydatabase system can be in-memory databases storing data in main memory.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, cause at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g., the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The subject matter described herein provides many technical advantages.For example, the current subject matter provides for a balance approachbetween savepoint execution and the consumption of log volume.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a system diagram illustrating an example database system foruse in connection with the current subject matter;

FIG. 2 is a system diagram illustrating an example database system thatcan support distribution of server components across multiple hosts forscalability and/or availability purposes for use in connection with thecurrent subject matter;

FIG. 3 is a diagram illustrating an architecture for an index server foruse in connection with the current subject matter;

FIG. 4 is a functional flow diagram illustrating an architecture tosupport load balancing between a primary database system and a secondarydatabase system;

FIG. 5 is a functional flow diagram depicting one example solution tomanaging load balancing in a HA/DR system for use in connection with thecurrent subject matter; and

FIG. 6 is a process flow diagram illustrating triggering of savepointsbased on log volume or other characteristics.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The current subject matter is directed to enhanced techniques fordatabase recovery. In particular, the current subject matter is directedto database recovery in which savepoints are asynchronously executedthereby enabling more rapid recovery of the database system.

FIG. 1 is a diagram 100 illustrating a database system 105 that can beused to implement aspects of the current subject matter. The databasesystem 105 can, for example, be an in-memory database in which allrelevant data is kept in main memory so that read operations can beexecuted without disk I/O and in which disk storage is required to makeany changes durables. The database system 105 can include a plurality ofservers including, for example, one or more of an index server 110, aname server 115, and/or an application server 120. The database system105 can also include one or more of an extended store server 125, adatabase deployment infrastructure (DDI) server 130, a data provisioningserver 135, and/or a streaming cluster 140. The database system 105 canbe accessed by a plurality of remote clients 145, 150 via differentprotocols such as SQL/MDX (by way of the index server 110) and/orweb-based protocols such as HTTP (by way of the application server 120).

The index server 110 can contain in-memory data stores and engines forprocessing data. The index server 110 can also be accessed by remotetools (via, for example, SQL queries), that can provide variousdevelopment environment and administration tools. Additional detailsregarding an example implementation of the index server 110 is describedand illustrated in connection with diagram 300 of FIG. 3.

The name server 115 can own information about the topology of thedatabase system 105. In a distributed database system, the name server115 can know where various components are running and which data islocated on which server. In a database system 105 with multiple databasecontainers, the name server 115 can have information about existingdatabase containers and it can also hosts the system database. Forexample, the name server 115 can manage the information about existingtenant databases. Unlike a name server 115 in a single-container system,the name server 115 in a database system 105 having multiple databasecontainers does not store topology information such as the location oftables in a distributed database. In a multi-container database system105 such database-level topology information can be stored as part ofthe catalogs of the tenant databases.

The application server 120 can enable native web applications used byone or more remote clients 150 accessing the database system 105 via aweb protocol such as HTTP. The application server 120 can allowdevelopers to write and run various database applications without theneed to run an additional application server. The application server 120can also used to run web-based tools 155 for administration, life-cyclemanagement and development. Other administration and development tools160 can directly access the index server 110 for, example, via SQL andother protocols.

The extended store server 125 can be part of a dynamic tiering optionthat can include a high-performance disk-based column store for very bigdata up to the petabyte range and beyond. Less frequently accessed data(for which is it non-optimal to maintain in main memory of the indexserver 110) can be put into the extended store server 125. The dynamictiering of the extended store server 125 allows for hosting of verylarge databases with a reduced cost of ownership as compared toconventional arrangements.

The DDI server 130 can be a separate server process that is part of adatabase deployment infrastructure (DDI). The DDI can be a layer of thedatabase system 105 that simplifies the deployment of database objectsusing declarative design time artifacts. DDI can ensure a consistentdeployment, for example by guaranteeing that multiple objects aredeployed in the right sequence based on dependencies, and byimplementing a transactional all-or-nothing deployment.

The data provisioning server 135 can provide enterprise informationmanagement and enable capabilities such as data provisioning in realtime and batch mode, real-time data transformations, data qualityfunctions, adapters for various types of remote sources, and an adapterSDK for developing additional adapters.

The streaming cluster 140 allows for various types of data streams(i.e., data feeds, etc.) to be utilized by the database system 105. Thestreaming cluster 140 allows for both consumption of data streams andfor complex event processing.

FIG. 2 is a diagram 200 illustrating a variation of the database system105 that can support distribution of server components across multiplehosts for scalability and/or availability purposes. This database system105 can, for example, be identified by a single system ID (SID) and itis perceived as one unit from the perspective of an administrator, whocan install, update, start up, shut down, or backup the system as awhole. The different components of the database system 105 can share thesame metadata, and requests from client applications 230 can betransparently dispatched to different servers 110 ₁₋₃, 120 ₁₋₃, in thesystem, if required.

As is illustrated in FIG. 2, the distributed database system 105 can beinstalled on more than one host 210 ₁₋₃. Each host 210 ₁₋₃ is a machinethat can comprise at least one data processor (e.g., a CPU, etc.),memory, storage, a network interface, and an operation system and whichexecutes part of the database system 105. Each host 210 ₁₋₃ can executea database instance 220 ₁₋₃ which comprises the set of components of thedistributed database system 105 that are installed on one host 210 ₁₋₃.FIG. 2 shows a distributed system with three hosts, which each run aname server 110 ₁₋₃, index server 120 ₁₋₃, and so on (other componentsare omitted to simplify the illustration).

FIG. 3 is a diagram 300 illustrating an architecture for the indexserver 110 (which can, as indicated above, be one of many instances). Aconnection and session management component 302 can create and managesessions and connections for the client applications 150. For eachsession, a set of parameters can be maintained such as, for example,auto commit settings or the current transaction isolation level.

Requests from the client applications 150 can be processed and executedby way of a request processing and execution control component 310. Thedatabase system 105 offers rich programming capabilities for runningapplication-specific calculations inside the database system. Inaddition to SQL, MDX, and WIPE, the database system 105 can providedifferent programming languages for different use cases. SQLScript canbe used to write database procedures and user defined functions that canbe used in SQL statements. The L language is an imperative language,which can be used to implement operator logic that can be called bySQLScript procedures and for writing user-defined functions.

Once a session is established, client applications 150 typically use SQLstatements to communicate with the index server 110 which can be handledby a SQL processor 312 within the request processing and executioncontrol component 310. Analytical applications can use themultidimensional query language MDX (MultiDimensional eXpressions) viaan MDX processor 322. For graph data, applications can use GEM (GraphQuery and Manipulation) via a GEM processor 316, a graph query andmanipulation language. SQL statements and MDX queries can be sent overthe same connection with the client application 150 using the samenetwork communication protocol. GEM statements can be sent using abuilt-in SQL system procedure.

The index server 110 can include an authentication component 304 thatcan be invoked with a new connection with a client application 150 isestablished. Users can be authenticated either by the database system105 itself (login with user and password) or authentication can bedelegated to an external authentication provider. An authorizationmanager 306 can be invoked by other components of the database system150 to check whether the user has the required privileges to execute therequested operations.

Each statement can processed in the context of a transaction. Newsessions can be implicitly assigned to a new transaction. The indexserver 110 can include a transaction manager 344 that coordinatestransactions, controls transactional isolation, and keeps track ofrunning and closed transactions. When a transaction is committed orrolled back, the transaction manager 344 can inform the involved enginesabout this event so they can execute necessary actions. The transactionmanager 344 can provide various types of concurrency control and it cancooperate with a persistence layer 346 to achieve atomic and durabletransactions.

Incoming SQL requests from the client applications 150 can be e receivedby the SQL processor 312. Data manipulation statements can be executedby the SQL processor 312 itself. Other types of requests can bedelegated to the respective components. Data definition statements canbe dispatched to a metadata manager 306, transaction control statementscan be forwarded to the transaction manager 344, planning commands canbe routed to a planning engine 318, and task related commands canforwarded to a task manager 324 (which can be part of a larger taskframework) Incoming MDX requests can be delegated to the MDX processor322. Procedure calls can be forwarded to the procedure processor 314,which further dispatches the calls, for example to a calculation engine326, the GEM processor 316, a repository 300, or a DDI proxy 328.

The index server 110 can also include a planning engine 318 that allowsplanning applications, for instance for financial planning, to executebasic planning operations in the database layer. One such basicoperation is to create a new version of a data set as a copy of anexisting one while applying filters and transformations. For example,planning data for a new year can be created as a copy of the data fromthe previous year. Another example for a planning operation is thedisaggregation operation that distributes target values from higher tolower aggregation levels based on a distribution function.

The SQL processor 312 can include an enterprise performance management(EPM) runtime component 320 that can form part of a larger platformproviding an infrastructure for developing and running enterpriseperformance management applications on the database system 105. Whilethe planning engine 318 can provide basic planning operations, the EPMplatform provides a foundation for complete planning applications, basedon by application-specific planning models managed in the databasesystem 105.

The calculation engine 326 can provide a common infrastructure thatimplements various features such as SQLScript, MDX, GEM, tasks, andplanning operations. The SQLScript processor 312, the MDX processor 322,the planning engine 318, the task manager 324, and the GEM processor 316can translate the different programming languages, query languages, andmodels into a common representation that is optimized and executed bythe calculation engine 326. The calculation engine 326 can implementthose features using temporary results 340 which can be based, in part,on data within the relational stores 332.

Metadata can be accessed via the metadata manager component 308.Metadata, in this context, can comprise a variety of objects, such asdefinitions of relational tables, columns, views, indexes andprocedures. Metadata of all these types can be stored in one commondatabase catalog for all stores. The database catalog can be stored intables in a row store 336 forming part of a group of relational stores332. Other aspects of the database system 105 including, for example,support and multi-version concurrency control can also be used formetadata management. In distributed systems, central metadata is sharedacross servers and the metadata manager 308 can coordinate or otherwisemanage such sharing.

The relational stores 332 form the different data management componentsof the index server 110 and these relational stores can, for example,store data in main memory. The row store 336, a column store 338, and afederation component 334 are all relational data stores which canprovide access to data organized in relational tables. The column store338 can stores relational tables column-wise (i.e., in a column-orientedfashion, etc.). The column store 338 can also comprise text search andanalysis capabilities, support for spatial data, and operators andstorage for graph-structured data. With regard to graph-structured data,from an application viewpoint, the column store 338 could be viewed as anon-relational and schema-flexible in-memory data store forgraph-structured data. However, technically such a graph store is not aseparate physical data store. Instead it is built using the column store338, which can have a dedicated graph API.

The row store 336 can stores relational tables row-wise. When a table iscreated, the creator can specify whether it should be row orcolumn-based. Tables can be migrated between the two storage formats.While certain SQL extensions are only available for one kind of table(such as the “merge” command for column tables), standard SQL can beused on all tables. The index server 110 also provides functionality tocombine both kinds of tables in one statement (join, sub query, union).

The federation component 334 can be viewed as a virtual relational datastore. The federation component 334 can provide access to remote data inexternal data source system(s) 354 through virtual tables, which can beused in SQL queries in a fashion similar to normal tables.

The database system 105 can include an integration of a non-relationaldata store 342 into the index server 110. For example, thenon-relational data store 342 can have data represented as networks ofC++ objects, which can be persisted to disk. The non-relational datastore 342 can be used, for example, for optimization and planning tasksthat operate on large networks of data objects, for example in supplychain management. Unlike the row store 336 and the column store 338, thenon-relational data store 342 does not use relational tables; rather,objects can be directly stored in containers provided by the persistencelayer 346. Fixed size entry containers can be used to store objects ofone class. Persisted objects can be loaded via their persisted objectIDs, which can also be used to persist references between objects. Inaddition, access via in-memory indexes is supported. In that case, theobjects need to contain search keys. The in-memory search index iscreated on first access. The non-relational data store 342 can beintegrated with the transaction manager 344 to extends transactionmanagement with sub-transactions, and to also provide a differentlocking protocol and implementation of multi version concurrencycontrol.

An extended store is another relational store that can be used orotherwise form part of the database system 105. The extended store can,for example, be a disk-based column store optimized for managing verybig tables, which ones do not want to keep in memory (as with therelational stores 332). The extended store can run in an extended storeserver 125 separate from the index server 110. The index server 110 canuse the federation component 334 to send SQL statements to the extendedstore server 125.

The persistence layer 346 is responsible for durability and atomicity oftransactions. The persistence layer 346 can ensure that the databasesystem 105 is restored to the most recent committed state after arestart and that transactions are either completely executed orcompletely undone. To achieve this goal in an efficient way, thepersistence layer 346 can use a combination of write-ahead logs, undoand cleanup logs, shadow paging and savepoints. The persistence layer346 can provide interfaces for writing and reading persisted data and itcan also contain a logger component that manages a recovery log.Recovery log entries can be written in the persistence layer 352 (inrecovery log volumes 352) explicitly by using a log interface orimplicitly when using the virtual file abstraction. The recovery logvolumes 352 can include redo logs which specify database operations tobe replayed whereas data volume 350 contains undo logs which specifydatabase operations to be undone as well as cleanup logs of committedoperations which can be executed by a garbage collection process toreorganize the data area (e.g. free up space occupied by deleted dataetc.).

The persistence layer 236 stores data in persistent disk storage 348which, in turn, can include data volumes 350 and/or recovery log volumes352 that can be organized in pages. Different page sizes can besupported, for example, between 4 k and 16 M. Data can be loaded fromthe disk storage 348 and stored to disk page wise. For read and writeaccess, pages can be loaded into a page buffer in memory. The pagebuffer need not have a minimum or maximum size, rather, all free memorynot used for other things can be used for the page buffer. If the memoryis needed elsewhere, least recently used pages can be removed from thecache. If a modified page is chosen to be removed, the page first needsto be persisted to disk storage 348. While the pages and the page bufferare managed by the persistence layer 346, the in-memory stores (i.e.,the relational stores 332) can access data within loaded pages.

In many applications, data systems may be required to support operationson a 24/7 schedule, and data system providers may be required toguarantee a minimum amount of downtime, that is time during which asystem is not able to fully support ongoing operations. When a system isrequired to ensure an agreed level of operational performance, it may bereferred to as a high availability system (“HA”). One solution toguarantee substantially continuous uptime with no, or very little,downtime is to maintain one or more hot-standby systems. A hot-standbysystem, or a backup system, is a system that may be activated quickly inthe event of a disruption causing one or more functions of a primaryoperational data system to fail. Such a disruption may be referred to asa disaster, and the process of restoring a data system to fulloperations may be referred to as disaster-recovery (“DR”).

A hot-standby system may be an exact replica of a primary operationalsystem that is capable of providing all the functions provided by theprimary operational system, or a hot-standby may be a system that iscapable of providing a minimum amount of essential functionality duringthe time required to restore the primary operational data system. Thetime it takes after a disaster to restore full, or minimum,functionality of a data system, for example by bringing a hot-standbyonline, is referred to as recovery time. In an effort to minimizerecovery time, and thereby downtime, a hot-standby system is typicallyin a state just short of fully operational. For example, a systemarchitecture may be implemented in which all functional systems of thehot-standby are active and operational, and all system and data changesor updates occur in the primary operational system and the hot-standbyat the exact same time. In such a case the only difference in the twosystems may be that the primary is configured to respond to userrequests and the secondary is not. In other hot-standby systems one ormore functions may be disabled until mission critical systems of thehot-standby are observed to be operating normally, at which time theremaining functions may be brought online.

In many applications, data systems may be required to provide promptresponses to users and applications that rely on the data managed by thedata system. Providers and designers of data systems may be required toguarantee a minimum average throughput over time, or an average maximumresponse time. The speed with which a data system responds to a requestfrom a user or an application may be dependent on many factors, but allsystems are limited in the number of requests they can handle in a givenperiod of time. When a data system manages a relatively large amount ofdata, and supports a relatively large number of users or applications,during high workloads a request may be queued, buffered or rejecteduntil sufficient system resources are available to complete the request.When this happens, average throughput goes down and average responsetime goes up. One solution to such a problem is to distribute theworkload across multiple processing systems. This is known as loadbalancing.

One drawback to load balancing and HA systems is that they may requireadditional processing systems, which in turn have a high cost. It isoften the case with certain data systems supporting critical functionsof an organization that additional systems are needed to perform bothload balancing and HA functionality to efficiently support continuousoperations. Given the redundant nature of DR systems, they are oftenleft undisturbed unless a disaster occurs. Thus, in some circumstances,it is desirable to implement and maintain a combination highavailability/disaster recovery (HA/DR) system with load balancing thatincludes both a primary operational system and a hot-standby system, andpotentially one or more tertiary systems. Such a combination systemallows for load balancing of workload between the processing systems ofboth the primary operational system and the hot-standby system, withoutdisrupting the ability of the HA/DR system to assume primaryfunctionality in the event of a disaster.

FIG. 4 is a functional flow diagram illustrating an architecture 400 tosupport load balancing between a primary database system, or primarysystem 405 a and a secondary database system, or secondary system 405 b,which serves as hot-standby to primary system 405 a. Each of the primarysystem 405 a and the secondary system 405 b may be a single instancesystem, similar to database system 105 depicted in FIG. 1, or each maybe a distributed variation of database system 105 as depicted in FIG. 2.Such an architecture 400 may be useful in a high availability datasystem, or in a disaster recovery system, or in a combination HA/DRsystem.

Each of the primary system 405 a and secondary system 405 b may includea load balancing functionality. Such load balancing functionality mayfor example be contained within a distinct load balancing server 470 aor 470 b. But, such load balancing functionality may be managed by anysuitable processing system. For example, the application server 120 ofthe primary system may also manage the load balancing of requests issuedto the application server of the primary system 405 a, sending requeststo the secondary system 405 b as necessary to maintain a welldistributed workload.

As depicted in FIG. 4, each of the primary system 405 a and thesecondary system 405 b includes a load balancing server 470 a and 470 bwhich respectively receive requests from user applications directed tothe primary system 405 a or the secondary system 405 b. Such request maycome from either admin tools 460 or web-based tools 450, or any otheruser application. Upon receiving a request a load balancing server, e.g.470 a, determines how to distribute the workload. As depicted loadbalancing server 470 a routes an SQL request 465 from admin tools 460 tothe index server 110 of the primary system 405 a, while routing an HTTPrequest 455 from web-based tools 450 to the application server 120 ofthe secondary system 405 b.

Load balancing of resources between a primary system 405 a and asecondary system 405 b can give rise to a number of complicating issues.For example, if either of the requests 455, 465 requires writing to oneor more data tables, or modifying a data table, then the two systems 405a, 405 b will diverge. After many instances of write requests beingdistributed between the primary system 405 a and the secondary system405 b, the two systems would be substantially different, and likelyunusable. In another example, an application request, e.g. 465, mayperform a write transaction that is followed by a read transaction, e.g.455, related to the data written by the write request 465. If the writerequest is allocated to the primary system 405 a, the read request wouldobtain a different result depending on whether the subsequent readtransaction is carried out by the primary system 405 a or by thesecondary system 405 b.

Load balancing in a HA/DR system, by distributing a portion of theworkload of a primary data system to a hot-standby or backup system mustbe done in a way that does not disturb the principal purpose of thebackup system, which is to substantially eliminate downtime in a highavailability system by enabling quick and efficient recovery ofoperations. In other words, as a rule load balancing cannot break thehot-standby. Given this principal purpose, any solution that enablesload balancing of workload between a primary system and a backup systemmust maintain the backup system in an identical, or nearly identical,state as the primary system. Such a solution should also avoid orprohibit any actions which may cause the state of the backup system tosubstantially diverge from the state of the primary system. In this way,in the event of a partial or total failure of the primary system due todisaster, the backup system can failover to a primary system mode withminimal or no impact to client applications.

FIG. 5 depicts one possible solution to managing load balancing in aHA/DR system 500. HA/DR system 500 includes a primary system 505 and asecondary system 510 and is capable of load balancing between primarysystem 505 and secondary system 510 without interfering with thehot-standby functionality of the secondary system 510. Each of primarysystem 505 and secondary system 510 may be single instance databasesystems similar to database system 105 depicted in FIG. 1, or adistributed variation of database system 105 as depicted in FIG. 2.Furthermore, each of primary system 505 and secondary system 510 maycomprise less, more or all the functionality ascribed to index server110, 300, name server 115, application server 120, extended store server125, DDI server 130, data provisioning server 135, and stream cluster140. But, for simplicity of illustration HA/DR system 500 has beensimplified to highlight certain functionality by merely distinguishingbetween processing control 555, 560 and a persistence layer 565, 570 ofeach respective system 505, 510.

A collection of clients may each maintain an open connection to both theprimary system 505 and the secondary system 525. For example, client 515maintains a read/write connection 520 to the primary system 505 and aread only connection 525 to the secondary system 510. Alternatively,client 515 may maintain a read/write connection with each of the primarysystem 505 and the secondary system 510, while processes within thesecondary system 510 itself prohibit execution of any requests thatrequire a write transaction upon the secondary system while it is inbackup mode. Management of load balancing of the workload required by aclient application executing at client 515 may be managed by the client515 application itself. Alternatively, a client 515 application maysubmit a query request to the primary system 505. A process control 555load balancing process executing on processor 545 then may determinewhere the query should be executed and replies to the client 515 withinstructions identifying which system the client 515 should issue thequery to.

Primary system 505 may include an in-memory database in whichsubstantially all actively used data may be kept and maintained in mainmemory 535 so that operations can be executed without disk input/outputoperations (I/O), which requires accessing disk storage.

Active operations of applications within processing control 555 maycause processor 545 to read and write data into main memory 535 or todisk in the persistence layer 565. Processing control 555 applicationscan also cause processor 545 to generate transaction logs (e.g., redolog, undo log, cleanup log, etc.) for capturing data transactions uponthe database, which processor 545 then persists in the log volumes 585and data volumes 575 respectively. As substantially all actively useddata may reside in-memory, processing control 555 may interact primarilywith data held in main memory while only resorting to data volumes 575for retrieving and writing less often used data. Additional processeswithin processing control 555 may be executed by processor 545 to ensurethat in-memory data is persisted in persistence layer 565, so that thedata is available upon restart or recovery.

Primary system 505 may be the primary operational system for providingthe various functionality necessary to support 24/7 operations for anorganization. Secondary system 510 may be a hot-standby, ready to comeonline with minimal recovery time so as to minimize downtime. Secondarysystem 510 may be an identical physical system as primary system 505,and may be configured in a substantially identical manner in order toenable the secondary system 510 to provide all the same functionality asprimary system 505. For example, processing control 560 may include allthe same applications and functionality as processing control 555, andpersistence layer 570 may include data volumes 580 and log volumes 590that are configured in an identical manner as data volumes 575 and logvolumes 585 respectively. Secondary system 510 may also include anin-memory database kept and maintained primarily in main memory 540.

Primary system 505 and secondary system 510 differ in that all requests,from client 515 or otherwise, that require a write transaction areexecuted only in primary system 505. Primary system 505 and secondarysystem 510 further differ in that all write transactions are prohibitedby the secondary system 510. In order to propagate changes to the dataor the underlying schema from the primary system 505 to the secondarysystem 510, processor 545 also replicates 530 transaction logs directlyto the process control 560 of the secondary system 510. Process control560 includes one or more applications that cause processor 550 to thenreplay the transaction logs replicated from the primary system 505,thereby replaying the transactions at the secondary system 510. Astransaction logs are replayed, the various transactions executed at theprimary system become reflected in the secondary system 510. In order toensure both the HA functionality and the load balancing functionality,replay of the transaction logs at the secondary system places data inmain memory 540, and also persists any data committed in the primarysystem to persistence layer 570 to be stored by data volumes 580. Replayof the transaction logs at the secondary system 510 may also results inthe transaction logs being persisted in log volumes 590.

Transaction logs (e.g., redo logs, undo logs, cleanup logs, etc.) in thelog volumes 585 may be replicated in different ways. Where maintaining astandby system in as close to the same state as the primary system is animportant factor, logs may be replicated synchronously meaning that theprimary system will not commit a transaction until the secondarysuccessfully responds to the log replication. Such an arrangement slowsperformance of the primary system. Conversely, where performance of aprimary system is a priority, logs may be replicated asynchronously, inwhich case the primary operation proceeds with committing transactionswithout waiting for a response. Various tradeoffs can be made betweenthese two scenarios to achieve a proper level of performance whileensuring replication of critical data.

It will be appreciated from the detailed description above that such asecondary system in standby mode, such as secondary system 510, can onlybe as current as its most recently replayed transaction logs.Transaction logs are replicated and replayed at the secondary system 510only after a transaction executes in the primary system 505. Secondarysystem 510, therefore, is always slightly behind an associated primarysystem 515. Also, there is no guarantee that a query routed to theprimary system in a load balancing effort will be executed before,during or after a particular transaction log is replayed. Thus, thestate of the primary system 505 and the state of the secondary systemwill rarely if ever be identical. But, by addressing certain concerns,secondary system 510 may be kept in a state substantially close to thesame state as the primary system 505 such that the workload required bymany operations can be supported by the secondary 510.

With the HA/DR system 500, an initial copy on the primary system 505 canbe shipped to the secondary system 510 that can serve as a startingpoint, where both the primary system 505 and the secondary system 510have identical data, before transaction log replay commences tosynchronize all future changes from the primary system 505 to thesecondary system 510.

As noted above, the data of the primary system 505 (also referred to asthe primary system data) can comprise data volumes 350, 575 comprising adata store together with undo and cleanup log and recovery log volumes352, 590 comprising the recovery log. Other types of storagearrangements can be utilized depending on the desired configuration. Thedata store can comprise a snapshot of the corresponding databasecontents as of the last system savepoint. System savepoints (also knownin the field of relational database servers as checkpoints) can beperiodically or manually generated and provide a point at which therecovery log can be truncated.

The savepoint can, in some variations, include an undo log oftransactions which were open in the savepoint and/or a cleanup log oftransactions which were committed in the savepoint but not yet garbagecollected (i.e., data which has been deleted by these transactions hasbeen marked as deleted but has not been deleted in a physical manner toassure multiversion concurrency control).

The recovery log can comprise a log of all changes to the databasecontents (i.e., the database system 105, the primary database 505 and/orthe secondary database 510, etc.) since the last system savepoint, suchthat when a database server is restarted, its latest state is restoredby replaying the changes from the recovery log on top of the last systemsavepoint. Typically, in a relational database system, the previousrecovery log is cleared whenever a system savepoint occurs, which thenstarts a new, empty recovery log that will be effective until the nextsystem savepoint. While the recovery log is processed, a new cleanup logis generated which needs to be processed as soon as the commit isreplayed to avoid a growing data area because of deleted but not garbagecollected data.

For read access in arrangements having a primary system 505 and asecondary system 510 such as illustrated and described in connectionwith FIGS. 4 and 5, a read transaction needs to able to see a consistentstate of the database state. This conditions requires the blocking ofgarbage collection processes for such data which the read transactioncould potentially see. As the garbage collection processing is part ofthe recovery log processing this would mean to block the recovery queueswhich would also mean that the secondary system 510 cannot be in syncwith the primary system 510 anymore, resulting in inacceptable takeovertimes in case of a failure of the primary system.

As part of a database system recovery/restart, after the savepointedstate of data is restored, and before processing of the recovery logcommences, all cleanup logs can be iterated through and, inimplementations using a history manager, passed to the history managerfor asynchronous garbage collection processing.

In addition, it can be checked if there are older versions of thecleanup log present in the savepoint which need to be processedsynchronously with regard to the recovery log. In such cases, recoverylog processing can wait until garbage collection of old versions ofcleanup logs finish. However, recovery log processing can commence whenthere are newer versions of cleanup logs for garbage collection. Incases in which no old versions of cleanup logs exist, recovery logreplay can start immediately after the cleanup log from the savepointhas been passed to the history manager.

In some implementations, savepoints can be written during log replay bythe secondary system 510. However, with some arrangements, it is notpossible to write savepoints on the secondary system 510 in the samemanner as on the primary system 510. In the primary system 505, thesavepoint will, at the start of an exclusive phase, acquire a consistentchange exclusive lock to ensure that it sees a consistent state of data(i.e., no consistent Change running, etc.). However, during recovery,the info of the consistent changes is missing, so the only position atwhich no consistent change is known to be running is a savepoint logentry that is written by the primary savepoint (i.e., the savepoint onthe primary system 505). Synchronized redo replay entry was adopted toensure that first, all redo operations are executed up to this savepointlog entry log position, then the savepoint is executed, and afterwardsthe replay continues. With larger database systems with high load, itcan take several minutes to execute the savepoint, which will block thelog replay on the secondary system 510, up to the point that the replaycannot be executed on the secondary system 510 at the same speed as itis generated on the primary system 505.

A typical savepoint can have three phases. First, in the pre-criticalphase all modified pages can be iterated through and flushed to physicalpersistence (i.e., disk, etc.). Second, a critical phase can block allparallel updates and triggers all the remaining I/O to ensure theconsistent state of data. Lastly, a post-critical phase can wait for allremaining I/O.

With the primary system 505 (i.e., the online database system, etc.),only the second phase can have an influence on the parallel workload.And this phase can be short, as most of the pages should already beflushed in the first phase and I/O is only triggered for the remainingpages to ensure the consistent state of data and do not wait for I/O.During log replay, with conventional systems, all three phases can beexecuted synchronously which results in the log replay being blockeduntil all three phases are complete.

As noted above, with the current arrangement, the savepoint can beasynchronously executed during log replay. The first phase describedabove can be skipped such that log replay right can be continued aftersecond phase is finished, so there is no need to wait for any I/O. Thisoptimization, while providing enhanced recovery time, can lead toincreased memory consumption due to the shadow pages for all the pageI/O that is triggered during the second phase. To address thissituation, a continuous page flusher can be used that runs in parallelto the recovery and can actively flush modified pages to disk, tocompensate the first phase so that the amount of pages that have to beflushed in the savepoint can be limited. A predefined time threshold canbe set such that all modified pages that have not been modified sincethe predefined time threshold are flushed to disk (i.e., a time windowcan be defined which specifies when to flush pages to disk, etc.). Thepage flusher can be, for example, a thread that runs in a defined timeinterval and flushes all modified pages that are not modified for adefined time threshold.

It is explicitly allowed and expected that read statements that arerouted to the secondary system 510 can see an older state of data (logreplay can be lag behind the primary system 505). The goal here is thatthe visibility of the secondary system 510 can be as possible to theprimary system 505. The user can specify a maximum acceptable result lagfor the secondary system 510 relative to the primary system 505 (e.g., 1minute, etc.). If the replay by the secondary system 510 is laggingbehind more than 1 minute, the read statement will not be executed onthe secondary system 510 but rerouted back to the primary system 505.With some savepoint during recovery implementations, every savepoint(e.g., every 5 minutes, etc.) the result lag can increase as long as thelog replay is blocked by the savepoint, and this blocking can lastseveral minutes in systems with a high update load. Such an arrangementcan lead to a situation in which the customer cannot utilize thesecondary system 510, as the secondary system 510 does not meet therequired maximum result lag. In contrast, with the current optimization,given the independence of the savepoint, there is no substantialinfluence on the result lag.

There can be various parameters that control savepoints. For example, asavepoint interval can be defined which specifies how often (e.g., everyfive minutes by default, etc.) a savepoint is to be executed. Anotherparameter can specify a maximum time spent before the critical phasebefore entering savepoints critical phase. A third parameter can specifya threshold to enter the critical phase if disk flush duringnon-critical phase is faster than a specified threshold. Theseparameters can be set in order to keep critical phase as short aspossible because critical phase blocks page modifications and thereforeeffects system performance. However, a redo log needs to be writtenwithin one savepoint cycle and has to be kept until the savepoint isexecuted and the log has been backed up. Executing savepoints with toofew frequency might increase size of log volume too much and could causeLOG FULL situations. This is especially true for mass operations likemass import writing much amount of redo log. In addition, log startingfrom the last savepoint needs to be replayed after a system crash (crashrecovery), which makes restart times slower if the amount of log is toolarge.

The parameters described above, and optionally some user-specifiedparameters can improve performance if the system resources (memory,disk, CPUs, etc.) as well as the system load remain constant. However,in most cases, the system load will vary over time and, in virtualmachine environments, available resources may change. To take suchvariations into account, system behavior can be monitored. Suchmonitoring can include, for example, monitoring the amount of loggenerated, monitoring the filling of specific log areas as well as thenumber/size of modified pages which need to be written to disk, andtimes measured for previous savepoints. Based on some or all of suchmonitored metrics, the system will self-adapt the parameters describedabove to minimize critical-phase time (e.g. blocking time) as well asamount of log (lowering risk of disk-full and startup time after fromcrash-recovery).

FIG. 6 is a diagram 600 on which, at 610, each of a plurality ofdatabase transactions are logged in a log. During such logging, at 620,one or more characteristics of the log are monitored. Based on themonitoring and when a pre-defined condition is met, at 630, a savepointis triggered. Such savepoint can override or otherwise accelerate anyother savepoints that have been set by parameter or otherwise whichwould have been triggered.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, the subject matter describedherein may be implemented on a computer having a display device (e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor) fordisplaying information to the user and a keyboard and a pointing device(e.g., a mouse or a trackball) and/or a touch screen by which the usermay provide input to the computer. Other kinds of devices may be used toprovide for interaction with a user as well; for example, feedbackprovided to the user may be any form of sensory feedback (e.g., visualfeedback, auditory feedback, or tactile feedback); and input from theuser may be received in any form, including acoustic, speech, or tactileinput.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such a phrase isintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” In addition, use of the term “based on,” aboveand in the claims is intended to mean, “based at least in part on,” suchthat an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A computer-implemented method comprising: loggingeach of a plurality of database transactions in a log; monitoring,concurrent with the logging, one or more characteristics of the log; andtriggering, based on the monitoring, a savepoint when a pre-definedcondition is met.
 2. The method of claim 1, wherein the triggeredsavepoint overrides or accelerates a savepoint that would have otherwisebeen triggered based on pre-specified parameters.
 3. The method of claim1, wherein the monitoring monitors an absolute size of the log, andwherein the pre-defined condition specifies a value for the absolutesize of the log.
 4. The method of claim 1, wherein the monitoringmonitors a filling degree of the log, and wherein the pre-definedcondition specifies a value for the filling degree of the log.
 5. Themethod of claim 1, wherein the monitoring monitors an amount of logtransaction written since a last savepoint, and wherein the pre-definedcondition specifies a value for the amount of log transactions writtensince the last savepoint.
 6. The method of claim 1, wherein themonitoring monitors an amount of time since a last savepoint, andwherein the pre-defined condition specifies a value for the amount oftime written since the last savepoint.
 7. The method of claim 1, whereinthe database system comprises an in-memory database storing data in mainmemory.
 8. The method of claim 1, wherein the database system comprisesa primary database system and an associated secondary database system,wherein read statements are routed to the secondary database systemuntil such time as a result lag between the primary database system isbeyond a pre-defined lag threshold relative to the secondary databasesystem.
 9. The method of claim 8, wherein both of the primary databasesystem and the associated secondary database system are in-memorydatabases storing data in main memory.
 10. The method of claim 1,wherein the monitoring monitors (i) an amount of log generated, (ii)filling of specific log areas, (iii) a number and/or size of modifiedpages which need to be written to disk, and (iv) and times measured forprevious savepoints; wherein, based on such monitoring, the savepoint istriggered to minimize critical-phase time and an amount of log.
 11. Asystem comprising: at least one processor; and memory storinginstructions which, when executed by the at least one processor, resultin operations comprising: logging each of a plurality of databasetransactions in a log; monitoring, concurrent with the logging, one ormore characteristics of the log; and triggering, based on themonitoring, a savepoint when a pre-defined condition is met.
 12. Thesystem of claim 11, wherein the triggered savepoint overrides oraccelerates a savepoint that would have otherwise been triggered basedon pre-specified parameters.
 13. The system of claim 11, wherein themonitoring monitors an absolute size of the log, and wherein thepre-defined condition specifies a value for the absolute size of thelog.
 14. The system of claim 11, wherein the monitoring monitors afilling degree of the log, and wherein the pre-defined conditionspecifies a value for the filling degree of the log.
 15. The system ofclaim 11, wherein the monitoring monitors an amount of log transactionwritten since a last savepoint, and wherein the pre-defined conditionspecifies a value for the amount of log transactions written since thelast savepoint.
 16. The system of claim 11, wherein the monitoringmonitors an amount of time since a last savepoint, and wherein thepre-defined condition specifies a value for the amount of time writtensince the last savepoint.
 17. The system of claim 11, wherein thedatabase system comprises an in-memory database storing data in mainmemory.
 18. The system of claim 11, wherein the database systemcomprises a primary database system and an associated secondary databasesystem, wherein read statements are routed to the secondary databasesystem until such time as a result lag between the primary databasesystem is beyond a pre-defined lag threshold relative to the secondarydatabase system.
 19. The system of claim 18, wherein both of the primarydatabase system and the associated secondary database system arein-memory databases storing data in main memory.
 20. The system of claim11, wherein the monitoring monitors (i) an amount of log generated, (ii)filling of specific log areas, (iii) a number and/or size of modifiedpages which need to be written to disk, and (iv) and times measured forprevious savepoints; wherein, based on such monitoring, the savepoint istriggered to minimize critical-phase time and an amount of log.